[1]刘凌,郭剑,韩崇. 面向不平衡数据的模糊支持向量机[J].计算机技术与发展,2015,25(11):38-43.
　 Fuzzy Support Vector Machine for Imbalanced Data[J].,2015,25(11):38-43.
点击复制

 面向不平衡数据的模糊支持向量机()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年11期
页码:
38-43
栏目:
智能、算法、系统工程
出版日期:
2015-11-10

文章信息/Info

Title:
 Fuzzy Support Vector Machine for Imbalanced Data
文章编号:
1673-629X（2015)11-0038-06
作者:
 刘凌; 郭剑; 韩崇
 南京邮电大学 计算机学院,
关键词:
 ; 支持向量机; 模糊支持向量机; 不平衡数据集; 样本密度
Keywords:
 ; support vector machine; FSVM; imbalanced data; sample density
分类号:
TP31
文献标志码:
A
摘要:
 对于不平衡数据集,传统模糊支持向量机存在分类敏感等问题,且确定样本隶属度时大多只考虑距离因素,不能精准地反映样本点的重要程度,容易造成分类结果的偏差.文中提出一种改进的模糊支持向量机,在确定样本隶属度时,根据样本密度区分出不同类别的样本点,并分别赋予不同的隶属度值,提高了支持向量点的权重,降低了噪声点和孤立点对分类性能的影响.同时,进一步引入了不平衡类调节因子,以提高不平衡数据集的分类精度.实验结果表明,相比已有模糊支持向量机,该方法对于包含较多孤立点和噪声点的不平衡数据集具有更好的分类效果.
Abstract:
 Traditional Fuzzy Support Vector Machines (FSVM) are sensitive to imbalanced data. They compute their fuzzy memberships mainly according to the factor of distance,which can not reflect the importance of the samples precisely and may lead to an error of classi-fication results. To these problems,an improved FSVM is proposed in this paper. In the proposed FSVM,samples are firstly separated into different categories based on sample densities, and then they are assigned different fuzzy memberships. This method may improve the weight of support vectors and reduce the influence of outlier and noise points. Furthermore,the imbalanced factor is introduced to improve the classification precision of imbalanced data. The experimental results show that the improved FSVM has better performance for imbal-anced data with more outlier and noise points.

相似文献/References:

[1]李雷 张建民.一种改善的基于支持向量机的边缘检测算子[J].计算机技术与发展,2010,(03):125.
　LI Lei,ZHANG Jian-min.An Improved Edge Detector Using the Support Vector Machines[J].,2010,(11):125.
[2]陈俏 曹根牛 陈柳.支持向量机应用于大气污染物浓度预测[J].计算机技术与发展,2010,(01):247.
　CHEN Qiao,CAO Gen-niu,CHEN Liu.Application of Support Vector Machine to Atmospheric Pollution Prediction[J].,2010,(11):247.
[3]李晶 姚明海.基于支持向量机的语义图像分类研究[J].计算机技术与发展,2010,(02):75.
　LI Jing,YAO Ming-hai.Research of Semantic Image Classification Based on Support Vector Machine[J].,2010,(11):75.
[4]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
　JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(11):17.
[5]曹庆璞 董淑福 罗赟骞.网络时延的混沌特性分析及预测[J].计算机技术与发展,2010,(04):43.
　CAO Qing-pu,DONG Shu-fu,LUO Yun-qian.Chaotic Analysis and Prediction of Internet Time- Delay[J].,2010,(11):43.
[6]路川 胡欣杰.区域航空市场航线客流量预测研究[J].计算机技术与发展,2010,(04):84.
　LU Chuan,HU Xin-jie.Analysis of Regional Airline Passenger Forecast Title[J].,2010,(11):84.
[7]黄炜 黄志华.一种基于遗传算法和SVM的特征选择[J].计算机技术与发展,2010,(06):21.
　HUANG Wei,HUANG Zhi-hua.Feature Selection Based on Genetic Algorithm and SVM[J].,2010,(11):21.
[8]孙秋凤.microRNA计算识别中的模式识别技术[J].计算机技术与发展,2010,(06):97.
　SUN Qiu-feng.Pattern Recognition Technology for MicroRNA Identification[J].,2010,(11):97.
[9]刘振岩 王勇 陈立平 马俊杰 陈天恩.基于SVM的农业智能决策Web服务的研究与实现[J].计算机技术与发展,2010,(06):213.
　LIU Zhen-yan,WANG Yong,CHEN Li-ping,et al.Research and Implementation of Intelligence Decision Web Services Based on SVM for Digital Agriculture[J].,2010,(11):213.
[10]王李冬.一种新的人脸识别算法[J].计算机技术与发展,2009,(05):147.
　WANG Li-dong.A New Algorithm of Face Recognition[J].,2009,(11):147.
[11]吴家皋[][],余浩[] [],张雪英[]. 基于链接回溯的地理信息更新主题爬虫研究[J].计算机技术与发展,2014,24(07):52.
　WU Jia-gao[] [],YU Hao[] [],ZHANG Xue-ying[]. Study of Topic-driven Web Crawler for Geographic Information Updating Based on Link Backtracking[J].,2014,24(11):52.
[12]李雷,魏蕴婕. 结合模糊聚类与支持向量机的图像分割[J].计算机技术与发展,2014,24(07):88.
　LI Lei,WEI Yun-jie. Image Segmentation Combined FCM and SVM[J].,2014,24(11):88.
[13]李燕玲,苏一丹. 改进的二叉树支持向量机在多分类中的应用[J].计算机技术与发展,2014,24(07):181.
　LI Yan-ling,SU Yi-dan. Application of Improved Binary Tree Support Vector Machine in Multi-classification[J].,2014,24(11):181.
[14]李春生[],苏晓伟[],魏军[],等. 基于支持向量机的抽油机井功图识别研究[J].计算机技术与发展,2014,24(08):215.
　LI Chun-sheng[],SU Xiao-wei[],WEI Jun[],et al. Research on Diagrams Identification of Pumping Unit Based on Support Vector Machine[J].,2014,24(11):215.
[15]李妍坊,许歆艺,刘功申. 面向情感倾向性识别的特征分析研究[J].计算机技术与发展,2014,24(09):33.
　LI Yan-fang,XU Xin-yi,LIU Gong-shen. Research on Feature Analysis Oriented Text Sentiment Identification[J].,2014,24(11):33.
[16]鄂旭[] [],毕佳娜[],侯建[],等. 一种车牌智能定位方法研究[J].计算机技术与发展,2014,24(10):124.
　E Xu[] [],BI Jia-na[],HOU Jian[],et al. Research on a License Plate Intelligent Localization Method[J].,2014,24(11):124.
[17]李利杰[],张君华[],熊伟清[],等. 一种改进的支持向量机模型优化算法[J].计算机技术与发展,2014,24(12):114.
　LI Li-jie[],ZHANG Jun-hua[],XIONG Wei-qing[],et al. An Improved Algorithm for Model Optimization of Support Vector Machine[J].,2014,24(11):114.
[18]陈桂林,王生光,徐静妹,等. 基于GA和组合核的SVM入侵检测算法[J].计算机技术与发展,2015,25(02):148.
　CHEN Gui-lin,WANG Sheng-guang,XU Jing-mei,et al. Intrusion Detection Algorithm of SVM Based on GA and Composed Kernel Function[J].,2015,25(11):148.
[19]李琼,陈利. 一种改进的支持向量机文本分类方法[J].计算机技术与发展,2015,25(05):78.
　LI Qiong CHEN Li. An Improved Text Classification Method for Support Vector Machine[J].,2015,25(11):78.
[20]贾慧敏. 基于ML改进技术的IDS的设计与实现[J].计算机技术与发展,2015,25(06):114.
　JIA Hui-min. Design and Implementation of IDS Based on ML’ s Improved Technology[J].,2015,25(11):114.

更新日期/Last Update: 2015-12-18